1. Treewidth Algorithms and Networks

2. Treewidth2 Overview Historic introduction: Series parallel graphs Dynamic programming on trees Dynamic programming on series parallel graphs Treewidth Dynamic programming on graphs of small treewidth Finding tree decompositions

3. Treewidth3 Computing the Resistance With the Laws of Ohm R1R1 R2R2 R1R1 R2R2 Two resistors in series Two resistors in parallel 1789-1854

4. Treewidth4 Repeated use of the rules 6 2 6 2 5 1 Has resistance 4 1/6 + 1/2 = 1/(1.5) 1.5 + 1.5 + 5 = 8 1 + 7 = 8 1/8 + 1/8 = 1/4 1/6 + 1/2 = 1/(1.5) 1.5 + 1.5 + 5 = 8 1 + 7 = 8 1/8 + 1/8 = 1/4 7

5. Treewidth5 A tree structure P 62 62 5 1 7 PP SS 5 6622 71

6. Treewidth6 Carry on! Internal structure of graph can be forgotten once we know essential information about it! 6 2 6 2 5 17 4 ¼ + ¼ = ½

7. Treewidth7 Network is ‘series parallel graph’ 196*, 197*: many problems that are hard for general graphs are easy for –Trees –Series parallel graphs Many well-known problems Using tree structures for solving hard problems on graphs 1 Linear / polynomial time computable Linear / polynomial time computable e.g.: NP-complete e.g.: NP-complete

8. Treewidth8 Weighted Independent Set Independent set: set of vertices that are pair wise non-adjacent. Weighted independent set –Given: Graph G=(V,E), weight w(v) for each vertex v. –Question: What is the maximum total weight of an independent set in G? NP-hard

9. Treewidth9 Weighted Independent Set on Trees On trees, this problem can be solved in linear time with dynamic programming. Choose root r. For each v, T(v) is subtree with v as root. Write A(v) = maximum weight of independent set S in T(v) B(v) = maximum weight of independent set S in T(v), such that v  S.

10. Treewidth10 Recursive formulations If v is a leaf: –A(v) = w(v) –B(v) = 0 If v has children x 1, …, x r : A(v) = max{ w(v) + B(x 1 ) + … + B(x r ), A(x 1 ) + … A(x r ) } B(v) = A(x 1 ) + … A(x r )

11. Treewidth11 Linear time algorithm Compute A(v) and B(v) for each v, bottom- up. –E.g., in postorder Constructing corresponding sets can also be done in linear time.

12. Treewidth12 Second example: Weighted dominating set A set of vertices S is dominating, if each vertex in G belongs to S or is adjacent to a vertex in S. Problem: given a graph G with vertex weights, what is the minimum total weight of a dominating set in G? Again, NP-hard, but linear time on trees.

13. Treewidth13 Subproblems C(v) = minimum weight of dominating set S of T(v) D(v) = minimum weight of dominating set S of T(v) with v  S. E(v) = minimum weight of a set S of T(v) that dominates all vertices, except possibly v.

14. Treewidth14 Recursive formulations If v is a leaf, … If v has children x 1, …, x r : –C(v) = the minimum of: w(v) + E(x 1 ) + … + E(x r ) C(x 1 ) + … + C(x i-1 ) + D(x i ) + C(x i+1 ) + … + C(x r ), over all i, 1  i  r. –D(v) = w(v) + E(x 1 ) + … + E(x r ) –E(v) = min { w(v) + E(x 1 ) + … + E(x r ), C(x 1 ) + … + C(x r ) }

15. Treewidth15 Gives again a linear time algorithm Compute bottom up (e.g., postorder), and use another type of dynamic programming for the values C(v). Constructing sets can also be done in linear time

16. Treewidth16 Generalizing to series parallel graphs A 2-terminal graph is a graph G=(V,E) with two special vertices s and t, its terminals. A 2-terminal (multi)-graph is series parallel, when it is: –A single edge (s,t). –Obtained by series composition of 2 series parallel graphs –Obtained by parallel composition of 2 series parallel graphs

17. Treewidth17 Series composition s1s1 s2s2 t1t1 t2t2 s1s1 s2 s 2 =t 1 t2t2 +

18. Treewidth18 Parallel composition s2s2 t2t2 s1s1 t1t1 s1 t1 s 1 =s 2 t 1 =t 2 +

19. Treewidth19 Series Parallel Graphs have an SP-tree P 62 62 5 1 7 PP SS 5 6622 71

20. Treewidth20 G(i) Associate to each node i of SP tree a 2-terminal graph G(i). P PP SS 5 6622 71 62 62 5

21. Treewidth21 Maximum weighted independent set for series parallel graphs G(i), say with terminals s and t AA(i) = maximum weight of independent set S of G(i) with s  S, t  S BA(i) = maximum weight of independent set S of G(i) with s  S, t  S AB(i) = maximum weight of independent set S of G(i) with s  S, t  S BB(i) = maximum weight of independent set S of G(i) with s  S, t  S

22. Treewidth22 Maximum weighted independent set of series parallel graphs 2 Computing AA, AB, BA, BB for –Leaves of SP-tree: trivial –Series, parallel composition: case analysis, using values for sub-sp-graphs G(i 1 ), G(i 2 ) –E.g., series operation, s’ terminal between i 1 and i 2 AA(i) = max {AA(i 1 )+AA(i 2 ) – w(s’), AB(i 1 ) + BA(i 2 ) } O(1) time per node of SP-tree: O(n) total.

23. Treewidth23 Many generalizations Many other problems Other classes of graphs to which we can assign a tree-structure, including –Graphs of treewidth k, for small k.

24. Treewidth24 Tree decomposition A tree decomposition: –Tree with a vertex set associated to every node. –For all edges {v,w}: there is a set containing both v and w. –For every v: the nodes that contain v form a connected subtree. b c d e f aa b c c c d e h hf f a g g a g

25. Treewidth25 Tree decomposition A tree decomposition: –Tree with a vertex set associated to every node. –For all edges {v,w}: there is a set containing both v and w. –For every v: the nodes that contain v form a connected subtree. b c d e f aa b c c c d e h hf f a g g a g

26. Treewidth26 Treewidth (definition) Width of tree decomposition: Treewidth of graph G: tw(G)= minimum width over all tree decompositions of G. b c d e f aa b c c c d e h hf f a g g ab c d e f gh g a

27. Treewidth27 Some graphs have small treewidth Appearing in some applications (e.g., probabilistic networks) Trees have treewidth 1 Series Parallel graphs have treewidth 2. …

28. Treewidth28 Trees have treewidth one Choose a root r Take X r = {r}, and for each other node i: X i = {i, parent(i)} T with these bags gives a tree decomposition of width 1 a b c d e a b a c bd b e a

29. Treewidth29 Algorithms using tree decompositions Step 1: Find a tree decomposition of width bounded by some small k. –Discussed later Step 2. Use dynamic programming, bottom- up on the tree.

30. Treewidth30 Determining treewidth Treewidth problem (decision version): –Given: Graph G, integer k –Question: Is the treewidth of G at most k Treewidth problem (construction version): –Given: Graph G –Question: construct a tree decomposition of G with minimum width NP-complete (Arnborg, Proskurowski)  (n) time algorithm for fixed k (B.) Practical O(n) algorithms for k= 1, 2, 3 (Arnborg, Corneil, Proskurowski), k = 4 (Sanders; Hein and Koster) Practical O*(2 n ) algorithm for small graphs (B. et al.) Many (often good) heuristics Sometimes by construction, or construction comes from application (trees, sp-graphs, planar graphs with small diameter, …)

31. Treewidth31 Separator property i v w If both v and w not in X i, then v and w are not adjacent

32. Treewidth32 Nice tree decompositions Rooted tree, and four types of nodes i: –Leaf: leaf of tree with |X i | = 1. –Join: node with two children j, j’ with X i = X j = X j’. –Introduce: node with one child j with X i = X j  {v} for some vertex v –Forget: node with one child j with X i = X j  {v} for some vertex v There is always a nice tree decomposition with the same width.

33. Treewidth33 Define G(i) Nice tree decomposition. For each node i, G(i) subgraph of G, formed by all nodes in sets X j, with j=i or j a descendant of i in tree. –Notate: G(i) = ( V(i), E(i) ).

34. Treewidth34 Maximum weighted independent set on graphs with treewidth k For node i in tree decomposition, S  X i write –R(i, S) = maximum weight of independent set W of G(i) with W  X i = S, –  if such W does not exist

35. Treewidth35 Leaf nodes Let i be a leaf node. Say X i = {v}. R(i,{v}) = w(v) R(i,  ) = 0 v G(i) is a graph with one vertex

36. Treewidth36 Join nodes Let i be a join node with children j 1, j 2. R(i, S) = R(j 1, S) + R(j 2, S) – w(S). + =

37. Treewidth37 Introduce nodes Let i be a node with child j, with X i = X j  {v}. Let S  X j. R(i,S) = R(j,S). If v not adjacent to vertex in S: R(i,S  {v})=R(j,S) + w(v) If v adjacent to vertex in S: R(i,S  {v}) = – . v

38. Treewidth38 Forget nodes Let i be a node with child j, with X i = X j  {v}. Let S  X i. R(i,S) = max (R(j,S), R(j,S  {v})) v v

39. Treewidth39 Maximum weighted independent set on graphs with treewidth k For node i in tree decomposition, S  X i write –R(i, S) = maximum weight of independent set W of G(i) with W  X i = S, –  if such W does not exist Compute for each node i, a table with all values R(i, …). Each such table can be computed in O(2 k ) time when treewidth at most k. Gives O(n) algorithm when treewidth is (small) constant.

40. Treewidth40 Frequency assignment problem Given: –Graph G=(V,E) –Frequency set F(v)  N for all v  V –Cost function c(e,r,s), e = {v,w}, r a frequency of v, s a frequency of w Question –Find a function g with For all v  V: g(v)  F(v) The total sum over all edges e={v,w} of c(e,g(v),g(w)) is as small as possible

41. Treewidth41 Frequency assignment when treewidth is small Suppose sets F(v) are small Suppose G has small treewidth Algorithm exploits tree decomposition What tables are we computing? –Leaf: trivial –Introduce: … –Forget: projection –Join: sum but subtract double terms

42. Treewidth42 General method Compute a tree decomposition –E.g., with minimum degree heuristic –Make it nice –Use dynamic programming Works for many problems –Courcelle: those that can be formulated in monadic second order logic –Practical: TSP, frequency assignment, problems on planar graphs like dominating set, probabilistic inference

43. Treewidth43 A lemma Let ({X i | i  I},T) be a tree decomposition of G. Let Z be a clique in G. Then there is a j  I with Z  X j. –Proof: Take arbitrary root of T. For each v  Z, look at highest node containing v. Look at such highpoint of maximum depth.

44. Treewidth44 The minimum degree heuristic Repeat: –Take vertex v of minimum degree –Make neighbors of v a clique –Remove v, and repeat on rest of G –Add v with neighbors to tree decomposition N(v)N(v) N(v)N(v) vN(v)vN(v) A heuristic for treewidth Works often well A heuristic for treewidth Works often well

45. Treewidth45 Other heuristics Minimum fill-in heuristic –Similar to minimum degree heuristic, but takes vertex with smallest fill-in: Number of edges that must be added when the neighbours of v are made a clique Other choices of vertices, refining, using separators, …

46. Treewidth46 Representation as permutation A correspondence between tree decompositions and permutations of the vertices –Repeat: remove superfluous leaf bag, or take vertex that appears in 1 leaf bag and no other bag –Make neighbours of v =  (1) into a clique; recursively make tree decomposition of graph – v; add bag with v and neighbours Used in heuristics, and local search methods (e.g., taboo search, simulated annealing) and genetic algorithms

47. Treewidth47 Connection to Gauss eliminating Consider Gauss elimination on a symmetric matrix For n by n matrix M, let G M be the graph with n vertices, and edge (i,j) if M ij  0 If we eliminate a row and corresponding column, effect on G is: –Make neighbors of v a clique –Remove v

48. Treewidth48 Application: Probabilistic networks Lauritzen-Spiegelhalter algorithm for inference on probabilistic networks (belief networks) uses a tree decomposition of the moralized form of the network Underlying several modern decision support networks

49. Treewidth49

50. Designing a DP algorithm Methodology: 1.What are “partial certificates”? 2.What characterizes a partial certificate (essential for extending to full certificate)? Gives set of subproblems 3.Give recurrences for subproblems 4.Find order in which recurrences are evaluated; or use memorization 5.Give algorithm; possibly save memory or make construction version Treewidth50

51. Treewidth51 Conclusions Dynamic programming for graphs with tree- like structure Works for a large collection of problems, as long as there is (and we can find) such a structure…